Dataset: We use NYU-Depth-v2 [1]} and SUN-RGBD [2]} datasets to evaluate our presented model. NYU-Depth-v2 includes around 120K RGB-D images of 464 indoor scenes where only 1449 images from them have semantic labels. We follow the methods adopted in [3]} [4]} to use the standard 795 training pairs and 654 testing pairs. SUN-RGBD is another dataset obtained from the indoor scenes containing about 10K images(5285 images for training and 5050 images for testing). Since the dataset has both semantic labels and depth labels, the whole training set is utilized to train our model and the test set to evaluate our semantic predictions. Both of these two datasets are employed for comparing with the state-of-the-art methods.
